In the communications arena, one of the biggest challenges is to overcome crosstalk, noise, and other disturbances that interfere with signals. Whether the signals are transmitted over wires, cable, fiber optics, wireless, or other types of communication, the signals suffer from some level of interference.
Interference in the signal may lead to certain limitations of the communications system. For example, in wireless systems, such as cellular phones, interference may shorten the distance at which the signal can be reliably received and the clarity of the signal. As another example, in wire systems, such as digital subscriber lines (DSL), interference may shorten the distance at which the signal can be reliably received, i.e. limit loop reach. Interference may also decrease the bit rate of the data being transferred. Providers of telecommunications services recognize the need to monitor the quality of service provided to users of their network and to identify the causes of problems reported by their customers. This task, however, is complicated significantly by several factors.
Some of these factors include: the large number of network users, the large amount of data collected from the deployed lines, and the presence of competing providers in the same physical line plant. The coexistence of ILEC's (Incumbent Local Exchange Carriers) and CLEC's (Competitive Local Exchange Carriers) in the same cable binders, brought about by the federally mandated deregulation of local telecommunications markets, implies that the services deployed by one carrier may be disturbing the users of another carrier, who has no information about the source of this disturbance.
It is thus highly desirable to sort through the collected data and determine whether a specific line is being disturbed by external interference, such as AM radio stations, or by internal interference, such as another DSL service, and whether that offending service belongs to the same carrier or not. Unfortunately, with today's deployed monitoring technology, carriers are extremely limited in their ability to perform such diagnoses with adequate accuracy and reliability.
The following discussion outlines in detail many of the problems of digital subscriber line (DSL) technology and potential solutions thereto. However, the discussion merely uses DSL as one example of the many communications systems (e.g. wireline, wireless, optical, cable, etc.) in which the present invention may be used. Thus the present invention should not be limited to merely DSL communication systems.
As discussed in the next section and shown in the accompanying Figures, embodiments of the present invention provide such a solution by 1) a design using MIMO transfer functions and probabilistic cause-effect relationships, 2) in a multi-processor, event driven, computational architecture, and 3) algorithms that combine system identification of MIMO systems with propagation of multiple Bayesian hypothesis tests.